An adaptive communication scheme for bandwidth limited residential load forecasting

Guangrui Xie, Xi Chen, Yang Weng

Research output: Chapter in Book/Report/Conference proceedingConference contribution


While adding new capabilities, the distributed energy resource proliferation raises great concern about challenges such as dynamic fluctuations of voltages. For example, in a volatile setting with highly uncertain renewable generation and customer consumption, it is challenging to provide reliable power and voltage prediction for operational planning purposes to mitigate risks, e.g., over-voltages. In this paper, we propose an integrated Gaussian Process-based method (IGP) for electric load (consumption minus generation) prediction. For improving the forecasting accuracy, we use not only the data streams generated by the target customer but also those of relevant customers in the feeder system. An adaptive data communication rate controlling scheme is further proposed for dimension reduction of streaming data to address the situation when bandwidth limit enforces a constraint in some feeders. The goal is to make IGP with the same prediction precision but significantly less streaming data amount. The superior efficacy and efficiency of IGP and its enhanced variants are tested and verified on the standard IEEE 8-bus and 123-bus distribution test cases.

Original languageEnglish (US)
Title of host publication2017 North American Power Symposium, NAPS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538626993
StatePublished - Nov 13 2017
Event2017 North American Power Symposium, NAPS 2017 - Morgantown, United States
Duration: Sep 17 2017Sep 19 2017

Publication series

Name2017 North American Power Symposium, NAPS 2017


Other2017 North American Power Symposium, NAPS 2017
Country/TerritoryUnited States


  • Gaussian process
  • Load prediction
  • active learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Control and Optimization
  • Electrical and Electronic Engineering


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